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RapidMiner 8.0: Containerized Server Architecture and UI

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There have been some major advancements to the RapidMiner platform since this article was originally published. We’re on a mission to make machine learning more accessible to anyone. For more details, check out our latest release.

At RapidMiner we have been hard at work, with support from an enthusiastic and diverse beta group, on improving user performance and enhancing the platform as we revamped RapidMiner Server (now known as RapidMiner AI Hub). The new architecture will provide improved reliability and horizontal scalability, but more importantly, become the cornerstone upon which we will expand the platform.

RapidMiner, the software platform built for data science teams, delivering cutting-edge machines learning and deep learning algorithms to more than 250,000 data scientists worldwide, is proud to announce the upcoming launch of RapidMiner Server 8.0. The redesigned platform introduces new functionalities that improve the production, deployment, and management of enterprise data science projects and sets the tone for future releases as we move towards a containerized server architecture.

RapidMiner 8.0 will bring new features that offer a significant increase in scalability and stability, enhanced resource management and improved job queuing and monitoring UI, including:

  • Horizontal Scalability: A new distributed architecture means that now multiple Job Agents can be installed on multiple machines for process execution. This allows you to scale your environment as much as you require, easing your worries of future scalability as your data science teams grow and processes require more dedicated resources.
  • Highly Stable Environment: The distributed nature of containerized job executions allows for operational continuity even in case one ‘rogue’ process was to fail. The failing process is ‘sandboxed’ and can no longer affect the execution of other processes, creating a highly stable environment.
  • Flexible Resource Management: Enhanced configuration capabilities provide a more dedicated resource management. Each Job Agent is configured to use specific resources like memory and CPU cores when executing processes, allowing to flexibly share and limit the system’s resources among collaborating data scientists.
  • Improved Job Monitoring: With the new release comes an improved Server UI, providing a better experience to monitor jobs. New filters in the job monitor allow to check and confirm regular execution more easily and to troubleshoot faster in case something went wrong.

For more information, be sure to check the RapidMiner community. Over the next few days and weeks, we will be posting information about our new architecture, specific use cases, and public beta. Stay tuned!

As we mentioned above, there have been several major advancements to the RapidMiner platform since this post was originally published. Be sure to check out our latest release.